# initialisation code copied on file GEO872-session-01-solution.rmd
# packages:
## Default repository
local({r <- getOption("repos")
r["CRAN"] <- "http://cran.r-project.org"
options(repos=r)
})
check_pkg <- function(x)
{
if (!require(x,character.only = TRUE, quietly = TRUE))
{
install.packages(x,dep=TRUE)
if(!require(x,character.only = TRUE, quietly = TRUE)) stop("Package not found")
}
}
# Call check_pkg() to install/load the required packages.
check_pkg("here")
check_pkg("sf")
check_pkg("readr")
check_pkg("ggplot2")
check_pkg("dplyr")
check_pkg("lubridate") #for timezones
check_pkg("raster")
check_pkg("leaflet")
# Machine learning
check_pkg("caTools")
check_pkg("class")
check_pkg("forecast")
check_pkg("MLmetrics")
check_pkg("randomForest")
#https://cran.r-project.org/web/packages/rmdwc/rmdwc.pdf
check_pkg("rmdwc")
#rmdcountAddin()
#DATA FOLDER
#On Martine laptop:
# data_folder <- "D:/Documents/Master/UZH/cours/GEO880_Computational Movement Analysis/Project/FS23_880Project_G3/data"
#On Yelu laptop:
data_folder <- "D:/GEO880/Project"new length: Length of report (approx. 15000 char (incl. spaces, incl. References list, excl. Code listing), 20000 char max)
Report your work in a written project report. The report has two
functions:
– It shall serve you as documentation of what you did, such that at a
later stage you can use the report in one of your own projects.
– It will be used to evaluate and mark your project.
Your report shall: – cover how you went about investigating your research questions. Describe your data science ideas and how you implemented your ideas. – present the results of your study and discuss them in the light of your research questions. What have you achieved and what would be further steps for future research? – report problems and limitations you encountered along the way and the solutions you chose to overcome these, be it limitations with respect to the data sources, the tools or any other source of limitation. – discuss your data science choices in the light of the theory covered in the lectures, group works, and your reading assignments.
Good example: https://fbilan.github.io/GEO880_2020/Project_GEO880_Wysling_Biland.html
Literature review - list of travel modes considered in our study. - characteristics specific to the mean of transport
First the trajectories data will be prepared to be fed for machine learning: data cleaning, labelizing, values extracted.
Labels list: boat, train, etc.
#import and convert to sf object
posmo <- read_delim(here(data_folder, "posmo_2023-06-18.csv"), ",",show_col_types = FALSE) %>% st_as_sf(coords = c("lon_x", "lat_y"), crs=4326, remove = FALSE) |> st_transform(2056)
#clean and add columns
posmo$source <- "posmo"
posmo <- posmo %>%
rename("tmode_posmo" = "transport_mode")
posmo$tmode_manual <- NA
to_remove <- c("place_name", "lon_x", "lat_y", "user_id")
posmo <- posmo[ , !(names(posmo) %in% to_remove)]
#add East and North columns
coords <- posmo |> st_coordinates()
posmo <- posmo |>
mutate(E = coords[,1], N = coords[,2])
# Keep only Switzerland
ch_ll <- c(2460666, 1069416)
ch_ur <- c(2849000, 1300750)
posmo <- posmo %>%
filter((E > ch_ll[1] & E < ch_ur[1] & N > ch_ll[2] & N < ch_ur[2]))
#update timezone
posmo <- posmo %>%
mutate(
datetime = datetime %>% with_tz(tzone = "Europe/Zurich")
)
# Add heights
# The height comes from the Digital height model DHM25 provided by swisstopo. The raster has been preprocessed on QGIS to be converted into the epsg 2056 (previously 21781) to save calculation time.
# The resolution is 25m. To have more precise information, we could look at the swiss3DAlti if needed.
# Simplification: we did not consider height of soil compared to height of person or public transport.
# As it is a relative variable to calculate slope, it does not matter.
#Extract values from rasters:
#https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
dhm25 <- raster(here(data_folder, "dhm25_raster", "dhm25_2056.tif"))
rasValue <- extract(dhm25, posmo)
posmo$H <- rasValue
# #Adapt column order
# col_order <- c("source", "datetime", "weekday", "tmode_posmo", "tmode_manual" ,"E", "N", "H", "timelag", "steplength_m", "speed_ms", "slope", "geometry")
# posmo <- posmo[, col_order]
# posmo
# REMOVE:
# heights for plane
# rmeove outside of Switzerland (Freiburg and Basel)# Add column for travel mode
posmo$tmode_manual <- NA
#select boats segments
condition_boats <- posmo$datetime > as.POSIXct("2023-05-14 13:55:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-14 14:21:00", tz = "Europe/Zurich")
#select planes segments
condition_planes <- posmo$datetime > as.POSIXct("2023-06-17 11:12:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 12:22:00", tz = "Europe/Zurich")
#select cable car segments
condition_cable_cars <-
posmo$datetime > as.POSIXct("2023-04-12 09:12:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 09:18:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:02:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:10:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 12:20:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 12:27:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:18:10", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:24:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 13:11:45", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 13:20:00", tz = "Europe/Zurich")
# select ski lifts segments
condition_ski_lifts <-
posmo$datetime > as.POSIXct("2023-04-12 09:23:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 09:30:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 09:33:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 09:41:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 09:51:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 9:59:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 10:15:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:24:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 10:51:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:57:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:08:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:21:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:29:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:46:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:51:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 13:49:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 13:54:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 13:57:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 14:05:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:14:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:19:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:22:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:30:45", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:36:45", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:44:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:58:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:08:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:19:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:28:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:40:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:51:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:28:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:33:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:36:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:43:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:48:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:56:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:06:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:14:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:23:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:28:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:30:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:38:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 11:06:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 11:14:30", tz = "Europe/Zurich")
# select t-bar segments
condition_t_bars <-
posmo$datetime > as.POSIXct("2023-04-12 10:28:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:33:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 14:10:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 14:18:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:54:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 11:02:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 11:19:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 11:26:45", tz = "Europe/Zurich")
# select ski slopes segments
condition_skis <-
posmo$datetime > as.POSIXct("2023-04-12 09:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 09:33:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 09:41:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 9:51:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 09:59:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:15:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 10:24:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:28:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 10:33:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 10:51:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 10:57:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:08:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:21:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:29:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 11:46:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 11:51:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 13:49:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 13:54:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 13:57:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 14:05:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 14:10:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 14:18:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 14:48:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:12:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:14:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:19:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:22:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:30:45", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:36:45", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 09:44:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 09:58:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:08:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:17:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:28:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:40:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 10:51:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 10:54:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 11:02:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 11:25:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 15:40:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 15:59:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:24:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:28:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:33:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:36:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:43:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 9:48:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 09:56:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:06:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:14:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:23:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:28:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 10:30:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 10:38:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 11:06:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 11:14:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 11:19:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 11:26:45", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 11:42:30", tz = "Europe/Zurich")
#select kick scooters segments
condition_scooters <-
posmo$datetime > as.POSIXct("2023-05-04 07:36:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-04 07:42:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-24 22:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-24 22:10:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-24 19:48:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-24 20:00:00", tz = "Europe/Zurich")
#select bikes segments
condition_bikes <-
posmo$datetime > as.POSIXct("2023-05-02 08:31:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-02 08:36:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-02 11:49:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-02 12:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-02 17:44:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-02 18:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-15 15:57:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-15 16:32:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-01 07:37:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-01 07:53:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-01 18:38:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-01 18:52:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 07:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 08:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 17:32:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 17:50:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 18:02:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 18:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 18:09:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 18:35:00", tz = "Europe/Zurich")
#select train segments
condition_trains <-
posmo$datetime > as.POSIXct("2023-05-26 18:10:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-26 19:15:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-09 8:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-09 10:55:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-28 12:20:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-28 12:50:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-09 19:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 21:30:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-09 12:23:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 12:48:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-11 02:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-11 02:45:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-12 07:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-12 07:53:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-14 17:25:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-14 17:42:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 09:05:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 11:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 08:06:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 10:02:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-16 18:48:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-16 19:11:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-18 12:45:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-18 16:33:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 07:04:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 07:43:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 15:27:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 17:53:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-03-31 16:23:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-03-31 16:47:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-03 21:32:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-04 00:22:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-06 17:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-06 17:57:00", tz = "Europe/Zurich")
#select trams segments
condition_trams <-
posmo$datetime > as.POSIXct("2023-06-09 17:17:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 17:22:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-09 13:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 13:54:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 11:09:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 11:23:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-05 18:49:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-05 19:03:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-05 23:33:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-05 23:40:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-25 15:50:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-25 15:57:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 10:07:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 10:19:45", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 18:36:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 18:56:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-04 23:05:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-04 23:16:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-04 16:51:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-04 17:01:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-04 19:29:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-04 19:41:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-05 08:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-05 08:52:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-06 07:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-06 07:50:00", tz = "Europe/Zurich")
#select bus segments
condition_bus <-
posmo$datetime > as.POSIXct("2023-06-09 17:25:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 17:31:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-24 19:32:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-24 19:49:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-24 22:10:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-24 22:22:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-07 07:53:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-07 08:12:00", tz = "Europe/Zurich") | posmo$datetime > as.POSIXct("2023-06-07 08:35:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-07 08:45:00", tz = "Europe/Zurich") | posmo$datetime > as.POSIXct("2023-06-07 19:23:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-07 19:37:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 20:39:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 20:44:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 21:03:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 21:17:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 08:15:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 08:25:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 08:28:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 08:35:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-05 18:37:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-05 18:46:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-25 11:36:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-25 11:57:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-25 16:33:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-25 16:39:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-17 16:07:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 16:19:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-18 12:12:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-18 12:16:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-18 12:23:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-18 12:29:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-04 23:17:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-04 23:23:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 06:59:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 07:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 07:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 07:50:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-01 22:17:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-01 22:30:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 21:57:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 22:14:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-03-31 16:13:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-03-31 16:20:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-04 19:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-04 19:59:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-06 07:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-06 07:33:00", tz = "Europe/Zurich")
#select walking segments
condition_walks <-
posmo$datetime > as.POSIXct("2023-04-16 15:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-16 15:37:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-16 15:44:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-16 15:56:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-13 13:29:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-13 15:01:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-13 16:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-13 22:01:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-13 22:04:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-13 22:29:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-20 11:54:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-20 22:35:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-22 11:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-22 11:42:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-29 16:18:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-29 16:46:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-09 11:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 12:20:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-11 02:46:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-11 03:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-12 07:53:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-12 08:07:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-06 08:11:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-06 08:15:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-05 23:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-05 23:57:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 09:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 09:12:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-12 18:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-12 18:50:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-25 11:57:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-25 12:05:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-25 15:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-25 15:50:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 08:43:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 08:46:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-13 18:05:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-13 18:15:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 13:20:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 13:25:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 10:19:45", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 10:24:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 18:56:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 19:15:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-16 18:29:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-16 18:45:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-16 21:41:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-16 22:23:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-17 15:59:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 16:04:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-17 16:20:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 16:28:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-17 22:31:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 22:45:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-18 12:16:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-18 12:19:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-18 12:29:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-18 12:33:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-04 17:59:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-04 18:12:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-04 22:50:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-04 23:05:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 15:04:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 15:23:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 17:50:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 18:02:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 18:04:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 18:09:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 19:47:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 20:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-05-31 21:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-05-31 21:55:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-10 18:29:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-10 18:45:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-10 22:10:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-10 22:18:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-11 11:51:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-11 11:56:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-11 15:28:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-11 15:36:00", tz = "Europe/Zurich")
#select car segments
condition_cars <-
posmo$datetime > as.POSIXct("2023-06-11 15:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-11 17:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-24 14:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-24 17:30:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-10 13:40:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-10 17:33:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-09 21:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-09 22:30:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-14 07:10:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-14 08:00:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-14 17:44:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-14 18:10:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-13 18:00:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-13 19:15:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-15 14:25:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-15 21:41:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-15 07:20:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-15 08:04:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-16 23:06:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-16 23:40:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-17 14:34:30", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-17 14:42:30", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-04 17:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-04 17:59:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-03 21:50:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-03 22:18:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-06-02 18:02:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-06-02 18:43:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-06 21:30:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-06 22:37:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-09 09:50:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-09 12:05:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-10 08:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-10 09:39:00", tz = "Europe/Zurich") |
posmo$datetime > as.POSIXct("2023-04-10 18:45:00", tz = "Europe/Zurich") & posmo$datetime < as.POSIXct("2023-04-10 21:10:00", tz = "Europe/Zurich")
#final labelizing
posmo <- posmo %>%
mutate(tmode_manual = case_when(condition_walks ~ 'walk',
condition_boats ~ 'boat',
condition_planes ~ 'plane',
condition_trains ~ 'train',
condition_bikes ~ 'bike',
condition_scooters ~ 'kick-scooter',
condition_cars ~ 'car',
condition_bus ~ 'bus',
condition_trams ~ 'tram',
condition_t_bars ~ 't_bar',
condition_cable_cars ~ 'cable_car',
condition_ski_lifts ~ 'ski_lift',
condition_skis ~ 'ski',
is.na(posmo$tmode_manual) == TRUE ~ 'unclassified'
))
# Remove heights for plane (bc completely false):
posmo$H[posmo$tmode_manual == "plane"] <- NA
# select columns
posmo <- dplyr::select(posmo, c("source", "datetime", "tmode_manual", "E", "N", "H", "geometry"))## Load the data
gps_data_raw <- read_delim(here(data_folder, "yelu_dataset_all.csv"), ";")
## Use right coordinate system and preserve original E/N columns
gps_data_raw <- st_as_sf(gps_data_raw, coords = c("Longitude", "Latitude"), crs = 4326, remove = FALSE)
gps_data <- gps_data_raw %>% st_transform(crs = 2056)
gps_data$Datetime <- as.POSIXct(paste(as.Date(gps_data$Date, format = '%d.%m.%Y'), gps_data$Time), format = "%Y-%m-%d %H:%M:%S", tz = "UTC")
gps_data <- gps_data %>% mutate(Datetime = Datetime %>% with_tz(tzone = "Europe/Zurich"))
gps_data$source <- "gps"
# Extract E and N values
coords <- gps_data |> st_coordinates()
gps_data <- gps_data |>
mutate(Latitude = coords[,2], Longitude = coords[,1])
# Rename columns
colnames(gps_data)[11] <- "datetime"
colnames(gps_data)[3] <- "N"
colnames(gps_data)[4] <- "E"
colnames(gps_data)[8] <- "H"
# Add column for travel mode
gps_data$tmode_manual <- NA
gps_data <- dplyr::select(gps_data, c("source", "datetime", "N", "E", "H", "geometry", "tmode_manual"))
# dataFolder <- here::here("D:/GEO880/Project/")
# zurich_map <- st_read(file.path(dataFolder, "stadtkreis/stadtkreis/Stadtkreis.shp"))
# zurich_map <- st_transform(zurich_map, epsg = 2056)# Conditions for different travel modes
condition_bus <-
gps_data$datetime >= as.POSIXct("2023-04-21 16:18:35") & gps_data$datetime <= as.POSIXct("2023-04-21 16:23:40") |
gps_data$datetime >= as.POSIXct("2023-04-30 18:04:25") & gps_data$datetime <= as.POSIXct("2023-04-30 18:20:15") |
gps_data$datetime >= as.POSIXct("2023-04-30 19:46:20") & gps_data$datetime <= as.POSIXct("2023-04-30 19:59:25") |
gps_data$datetime >= as.POSIXct("2023-05-10 19:31:25") & gps_data$datetime <= as.POSIXct("2023-05-10 19:34:55") |
gps_data$datetime >= as.POSIXct("2023-05-10 22:43:00") & gps_data$datetime <= as.POSIXct("2023-05-10 22:56:05") |
gps_data$datetime >= as.POSIXct("2023-05-12 07:46:00") & gps_data$datetime <= as.POSIXct("2023-05-12 07:51:20") |
gps_data$datetime >= as.POSIXct("2023-05-26 07:45:20") & gps_data$datetime <= as.POSIXct("2023-05-26 07:50:25") |
gps_data$datetime >= as.POSIXct("2023-06-05 14:59:35") & gps_data$datetime <= as.POSIXct("2023-06-05 15:09:00") |
gps_data$datetime >= as.POSIXct("2023-06-05 15:17:55") & gps_data$datetime <= as.POSIXct("2023-06-05 15:27:40")
condition_tram <-
gps_data$datetime >= as.POSIXct("2023-04-21 17:18:55") & gps_data$datetime <= as.POSIXct("2023-04-21 17:26:05") |
gps_data$datetime >= as.POSIXct("2023-04-25 15:07:50") & gps_data$datetime <= as.POSIXct("2023-04-25 15:11:55") |
gps_data$datetime >= as.POSIXct("2023-04-25 17:40:50") & gps_data$datetime <= as.POSIXct("2023-04-25 17:47:20") |
gps_data$datetime >= as.POSIXct("2023-04-25 18:57:55") & gps_data$datetime <= as.POSIXct("2023-04-25 19:04:12") |
gps_data$datetime >= as.POSIXct("2023-04-25 19:50:45") & gps_data$datetime <= as.POSIXct("2023-04-25 19:54:25") |
gps_data$datetime >= as.POSIXct("2023-04-25 20:26:10") & gps_data$datetime <= as.POSIXct("2023-04-25 20:32:05") |
gps_data$datetime >= as.POSIXct("2023-04-29 18:06:45") & gps_data$datetime <= as.POSIXct("2023-04-29 18:12:20") |
gps_data$datetime >= as.POSIXct("2023-04-30 16:45:05") & gps_data$datetime <= as.POSIXct("2023-04-30 16:56:30") |
gps_data$datetime >= as.POSIXct("2023-04-30 17:46:25") & gps_data$datetime <= as.POSIXct("2023-04-30 18:00:00") |
gps_data$datetime >= as.POSIXct("2023-04-30 20:10:35") & gps_data$datetime <= as.POSIXct("2023-04-30 20:12:05") |
gps_data$datetime >= as.POSIXct("2023-05-01 12:56:05") & gps_data$datetime <= as.POSIXct("2023-05-01 13:04:10") |
gps_data$datetime >= as.POSIXct("2023-05-01 13:16:50") & gps_data$datetime <= as.POSIXct("2023-05-01 13:25:05") |
gps_data$datetime >= as.POSIXct("2023-05-01 13:25:55") & gps_data$datetime <= as.POSIXct("2023-05-01 13:29:40") |
gps_data$datetime >= as.POSIXct("2023-05-01 19:28:40") & gps_data$datetime <= as.POSIXct("2023-05-01 19:30:35") |
gps_data$datetime >= as.POSIXct("2023-05-06 13:42:15") & gps_data$datetime <= as.POSIXct("2023-05-06 13:54:15") |
gps_data$datetime >= as.POSIXct("2023-05-06 13:58:00") & gps_data$datetime <= as.POSIXct("2023-05-06 14:04:35") |
gps_data$datetime >= as.POSIXct("2023-05-06 15:51:20") & gps_data$datetime <= as.POSIXct("2023-05-06 15:59:40") |
gps_data$datetime >= as.POSIXct("2023-05-06 16:07:35") & gps_data$datetime <= as.POSIXct("2023-05-06 16:18:05") |
gps_data$datetime >= as.POSIXct("2023-05-06 16:27:00") & gps_data$datetime <= as.POSIXct("2023-05-06 16:33:40") |
gps_data$datetime >= as.POSIXct("2023-05-06 17:13:15") & gps_data$datetime <= as.POSIXct("2023-05-06 17:20:05") |
gps_data$datetime >= as.POSIXct("2023-05-07 17:32:15") & gps_data$datetime <= as.POSIXct("2023-05-07 17:34:00") |
gps_data$datetime >= as.POSIXct("2023-05-07 18:09:50") & gps_data$datetime <= as.POSIXct("2023-05-07 18:26:55") |
gps_data$datetime >= as.POSIXct("2023-05-07 18:43:35") & gps_data$datetime <= as.POSIXct("2023-05-07 18:46:25") |
gps_data$datetime >= as.POSIXct("2023-05-07 19:41:25") & gps_data$datetime <= as.POSIXct("2023-05-07 19:44:50") |
gps_data$datetime >= as.POSIXct("2023-05-07 19:47:25") & gps_data$datetime <= as.POSIXct("2023-05-07 20:09:15") |
gps_data$datetime >= as.POSIXct("2023-05-08 19:25:25") & gps_data$datetime <= as.POSIXct("2023-05-08 19:28:35") |
gps_data$datetime >= as.POSIXct("2023-05-08 19:49:45") & gps_data$datetime <= as.POSIXct("2023-05-08 19:53:25") |
gps_data$datetime >= as.POSIXct("2023-05-10 17:16:45") & gps_data$datetime <= as.POSIXct("2023-05-10 17:28:45") |
gps_data$datetime >= as.POSIXct("2023-05-10 17:52:15") & gps_data$datetime <= as.POSIXct("2023-05-10 17:53:15") |
gps_data$datetime >= as.POSIXct("2023-05-12 18:00:50") & gps_data$datetime <= as.POSIXct("2023-05-12 18:08:15") |
gps_data$datetime >= as.POSIXct("2023-05-13 17:39:25") & gps_data$datetime <= as.POSIXct("2023-05-13 17:41:25") |
gps_data$datetime >= as.POSIXct("2023-05-16 17:48:05") & gps_data$datetime <= as.POSIXct("2023-05-16 17:50:00") |
gps_data$datetime >= as.POSIXct("2023-05-17 14:27:15") & gps_data$datetime <= as.POSIXct("2023-05-17 14:35:00") |
gps_data$datetime >= as.POSIXct("2023-05-17 14:52:15") & gps_data$datetime <= as.POSIXct("2023-05-17 15:00:25") |
gps_data$datetime >= as.POSIXct("2023-05-17 15:22:40") & gps_data$datetime <= as.POSIXct("2023-05-17 15:30:10") |
gps_data$datetime >= as.POSIXct("2023-05-18 14:45:50") & gps_data$datetime <= as.POSIXct("2023-05-18 14:56:25") |
gps_data$datetime >= as.POSIXct("2023-05-18 16:00:10") & gps_data$datetime <= as.POSIXct("2023-05-18 16:07:10") |
gps_data$datetime >= as.POSIXct("2023-05-20 16:43:35") & gps_data$datetime <= as.POSIXct("2023-05-20 16:51:35") |
gps_data$datetime >= as.POSIXct("2023-05-20 17:00:30") & gps_data$datetime <= as.POSIXct("2023-05-20 17:07:45") |
gps_data$datetime >= as.POSIXct("2023-05-20 20:30:40") & gps_data$datetime <= as.POSIXct("2023-05-20 20:45:40") |
gps_data$datetime >= as.POSIXct("2023-05-21 19:10:00") & gps_data$datetime <= as.POSIXct("2023-05-21 19:14:15") |
gps_data$datetime >= as.POSIXct("2023-05-21 19:16:50") & gps_data$datetime <= as.POSIXct("2023-05-21 19:37:25") |
gps_data$datetime >= as.POSIXct("2023-05-21 20:25:00") & gps_data$datetime <= as.POSIXct("2023-05-21 20:29:10") |
gps_data$datetime >= as.POSIXct("2023-05-21 20:37:45") & gps_data$datetime <= as.POSIXct("2023-05-21 20:47:35") |
gps_data$datetime >= as.POSIXct("2023-05-23 17:43:35") & gps_data$datetime <= as.POSIXct("2023-05-23 17:57:50") |
gps_data$datetime >= as.POSIXct("2023-05-23 17:43:35") & gps_data$datetime <= as.POSIXct("2023-05-23 17:57:50") |
gps_data$datetime >= as.POSIXct("2023-05-24 13:19:10") & gps_data$datetime <= as.POSIXct("2023-05-24 13:24:35") |
gps_data$datetime >= as.POSIXct("2023-05-24 17:39:00") & gps_data$datetime <= as.POSIXct("2023-05-24 17:55:35") |
gps_data$datetime >= as.POSIXct("2023-05-25 14:46:30") & gps_data$datetime <= as.POSIXct("2023-05-25 14:55:30") |
gps_data$datetime >= as.POSIXct("2023-05-25 15:19:20") & gps_data$datetime <= as.POSIXct("2023-05-25 15:28:35") |
gps_data$datetime >= as.POSIXct("2023-05-26 13:17:05") & gps_data$datetime <= as.POSIXct("2023-05-26 13:31:20") |
gps_data$datetime >= as.POSIXct("2023-05-26 13:53:00") & gps_data$datetime <= as.POSIXct("2023-05-26 14:10:00") |
gps_data$datetime >= as.POSIXct("2023-05-26 14:16:00") & gps_data$datetime <= as.POSIXct("2023-05-26 14:18:05") |
gps_data$datetime >= as.POSIXct("2023-05-28 14:52:20") & gps_data$datetime <= as.POSIXct("2023-05-28 15:04:05") |
gps_data$datetime >= as.POSIXct("2023-05-28 15:22:40") & gps_data$datetime <= as.POSIXct("2023-05-28 15:38:50") |
gps_data$datetime >= as.POSIXct("2023-05-28 20:20:00") & gps_data$datetime <= as.POSIXct("2023-05-28 20:37:00") |
gps_data$datetime >= as.POSIXct("2023-05-28 20:45:05") & gps_data$datetime <= as.POSIXct("2023-05-28 20:56:10") |
gps_data$datetime >= as.POSIXct("2023-05-29 17:40:05") & gps_data$datetime <= as.POSIXct("2023-05-29 17:59:50") |
gps_data$datetime >= as.POSIXct("2023-05-29 18:19:55") & gps_data$datetime <= as.POSIXct("2023-05-29 18:27:35") |
gps_data$datetime >= as.POSIXct("2023-05-29 18:28:35") & gps_data$datetime <= as.POSIXct("2023-05-29 18:36:00") |
gps_data$datetime >= as.POSIXct("2023-05-29 18:56:35") & gps_data$datetime <= as.POSIXct("2023-05-29 19:09:30") |
gps_data$datetime >= as.POSIXct("2023-05-30 17:31:10") & gps_data$datetime <= as.POSIXct("2023-05-30 17:42:50") |
gps_data$datetime >= as.POSIXct("2023-05-30 19:04:55") & gps_data$datetime <= as.POSIXct("2023-05-30 19:48:50") |
gps_data$datetime >= as.POSIXct("2023-05-31 17:08:25") & gps_data$datetime <= as.POSIXct("2023-05-31 17:09:40") |
gps_data$datetime >= as.POSIXct("2023-05-31 17:34:25") & gps_data$datetime <= as.POSIXct("2023-05-31 17:42:25") |
gps_data$datetime >= as.POSIXct("2023-06-02 13:29:10") & gps_data$datetime <= as.POSIXct("2023-06-02 13:30:45") |
gps_data$datetime >= as.POSIXct("2023-06-02 13:36:25") & gps_data$datetime <= as.POSIXct("2023-06-02 13:50:55") |
gps_data$datetime >= as.POSIXct("2023-06-02 14:55:10") & gps_data$datetime <= as.POSIXct("2023-06-02 15:13:10") |
gps_data$datetime >= as.POSIXct("2023-06-04 16:58:15") & gps_data$datetime <= as.POSIXct("2023-06-04 17:00:00") |
gps_data$datetime >= as.POSIXct("2023-06-04 17:09:20") & gps_data$datetime <= as.POSIXct("2023-06-04 17:14:10") |
gps_data$datetime >= as.POSIXct("2023-06-04 18:18:35") & gps_data$datetime <= as.POSIXct("2023-06-04 18:22:25") |
gps_data$datetime >= as.POSIXct("2023-06-04 18:48:20") & gps_data$datetime <= as.POSIXct("2023-06-04 19:03:35") |
gps_data$datetime >= as.POSIXct("2023-06-05 15:37:15") & gps_data$datetime <= as.POSIXct("2023-06-05 15:40:15") |
gps_data$datetime >= as.POSIXct("2023-06-09 13:51:50") & gps_data$datetime <= as.POSIXct("2023-06-09 13:53:45") |
gps_data$datetime >= as.POSIXct("2023-06-10 18:35:10") & gps_data$datetime <= as.POSIXct("2023-06-10 18:48:30") |
gps_data$datetime >= as.POSIXct("2023-06-10 20:48:30") & gps_data$datetime <= as.POSIXct("2023-06-10 21:01:00") |
gps_data$datetime >= as.POSIXct("2023-06-12 19:07:00") & gps_data$datetime <= as.POSIXct("2023-06-12 19:14:45") |
gps_data$datetime >= as.POSIXct("2023-06-12 19:43:20") & gps_data$datetime <= as.POSIXct("2023-06-12 19:45:40")
condition_walk <-
gps_data$datetime >= as.POSIXct("2023-04-21 16:13:40") & gps_data$datetime <= as.POSIXct("2023-04-21 16:16:25") |
gps_data$datetime >= as.POSIXct("2023-04-25 15:13:05") & gps_data$datetime <= as.POSIXct("2023-04-25 15:18:00") |
gps_data$datetime >= as.POSIXct("2023-04-25 15:37:00") & gps_data$datetime <= as.POSIXct("2023-04-25 15:40:15") |
gps_data$datetime >= as.POSIXct("2023-04-25 19:05:05") & gps_data$datetime <= as.POSIXct("2023-04-25 19:08:25") |
gps_data$datetime >= as.POSIXct("2023-04-25 20:32:45") & gps_data$datetime <= as.POSIXct("2023-04-25 20:35:05") |
gps_data$datetime >= as.POSIXct("2023-04-29 21:41:45") & gps_data$datetime <= as.POSIXct("2023-04-29 22:19:40") |
gps_data$datetime >= as.POSIXct("2023-04-30 18:20:25") & gps_data$datetime <= as.POSIXct("2023-04-30 19:39:00") |
gps_data$datetime >= as.POSIXct("2023-05-01 13:31:50") & gps_data$datetime <= as.POSIXct("2023-05-01 19:22:20") |
gps_data$datetime >= as.POSIXct("2023-05-01 19:31:55") & gps_data$datetime <= as.POSIXct("2023-05-01 19:36:05") |
gps_data$datetime >= as.POSIXct("2023-05-06 14:06:10") & gps_data$datetime <= as.POSIXct("2023-05-06 14:09:40") |
gps_data$datetime >= as.POSIXct("2023-05-06 16:18:45") & gps_data$datetime <= as.POSIXct("2023-05-06 16:21:30") |
gps_data$datetime >= as.POSIXct("2023-05-06 16:23:35") & gps_data$datetime <= as.POSIXct("2023-05-06 16:26:25") |
gps_data$datetime >= as.POSIXct("2023-05-06 16:33:50") & gps_data$datetime <= as.POSIXct("2023-05-06 16:34:40") |
gps_data$datetime >= as.POSIXct("2023-05-06 17:29:25") & gps_data$datetime <= as.POSIXct("2023-05-06 17:32:15") |
gps_data$datetime >= as.POSIXct("2023-05-07 17:34:55") & gps_data$datetime <= as.POSIXct("2023-05-07 18:05:05") |
gps_data$datetime >= as.POSIXct("2023-05-07 18:30:50") & gps_data$datetime <= as.POSIXct("2023-05-07 18:42:30") |
gps_data$datetime >= as.POSIXct("2023-05-07 20:13:00") & gps_data$datetime <= as.POSIXct("2023-05-07 20:15:35") |
gps_data$datetime >= as.POSIXct("2023-05-08 18:56:05") & gps_data$datetime <= as.POSIXct("2023-05-08 19:21:30") |
gps_data$datetime >= as.POSIXct("2023-05-08 19:53:55") & gps_data$datetime <= as.POSIXct("2023-05-08 20:14:55") |
gps_data$datetime >= as.POSIXct("2023-05-10 17:10:10") & gps_data$datetime <= as.POSIXct("2023-05-10 17:14:10") |
gps_data$datetime >= as.POSIXct("2023-05-10 17:28:50") & gps_data$datetime <= as.POSIXct("2023-05-10 17:52:00") |
gps_data$datetime >= as.POSIXct("2023-05-10 18:11:15") & gps_data$datetime <= as.POSIXct("2023-05-10 18:14:00") |
gps_data$datetime >= as.POSIXct("2023-05-10 19:36:45") & gps_data$datetime <= as.POSIXct("2023-05-10 19:42:45") |
gps_data$datetime >= as.POSIXct("2023-05-10 22:56:30") & gps_data$datetime <= as.POSIXct("2023-05-10 23:04:50") |
gps_data$datetime >= as.POSIXct("2023-05-12 07:52:05") & gps_data$datetime <= as.POSIXct("2023-05-12 07:54:35") |
gps_data$datetime >= as.POSIXct("2023-05-12 17:28:25") & gps_data$datetime <= as.POSIXct("2023-05-12 17:30:45") |
gps_data$datetime >= as.POSIXct("2023-05-12 18:25:50") & gps_data$datetime <= as.POSIXct("2023-05-12 18:28:20") |
gps_data$datetime >= as.POSIXct("2023-05-13 17:19:15") & gps_data$datetime <= as.POSIXct("2023-05-13 17:39:00") |
gps_data$datetime >= as.POSIXct("2023-05-13 17:41:40") & gps_data$datetime <= as.POSIXct("2023-05-13 17:45:25") |
gps_data$datetime >= as.POSIXct("2023-05-16 17:44:00") & gps_data$datetime <= as.POSIXct("2023-05-16 17:47:00") |
gps_data$datetime >= as.POSIXct("2023-05-16 17:51:20") & gps_data$datetime <= as.POSIXct("2023-05-16 17:54:05") |
gps_data$datetime >= as.POSIXct("2023-05-17 15:44:15") & gps_data$datetime <= as.POSIXct("2023-05-17 15:48:05") |
gps_data$datetime >= as.POSIXct("2023-05-18 14:21:00") & gps_data$datetime <= as.POSIXct("2023-05-18 14:40:35") |
gps_data$datetime >= as.POSIXct("2023-05-18 14:57:00") & gps_data$datetime <= as.POSIXct("2023-05-18 15:56:00") |
gps_data$datetime >= as.POSIXct("2023-05-18 16:11:45") & gps_data$datetime <= as.POSIXct("2023-05-18 16:14:30") |
gps_data$datetime >= as.POSIXct("2023-05-20 16:39:20") & gps_data$datetime <= as.POSIXct("2023-05-20 16:41:00") |
gps_data$datetime >= as.POSIXct("2023-05-20 17:09:00") & gps_data$datetime <= as.POSIXct("2023-05-20 20:30:00") |
gps_data$datetime >= as.POSIXct("2023-05-20 20:49:40") & gps_data$datetime <= as.POSIXct("2023-05-20 20:53:35") |
gps_data$datetime >= as.POSIXct("2023-05-21 19:37:35") & gps_data$datetime <= as.POSIXct("2023-05-21 20:24:05") |
gps_data$datetime >= as.POSIXct("2023-05-21 20:48:00") & gps_data$datetime <= as.POSIXct("2023-05-21 20:52:20") |
gps_data$datetime >= as.POSIXct("2023-05-23 17:34:05") & gps_data$datetime <= as.POSIXct("2023-05-23 17:36:25") |
gps_data$datetime >= as.POSIXct("2023-05-23 17:58:45") & gps_data$datetime <= as.POSIXct("2023-05-23 19:16:00") |
gps_data$datetime >= as.POSIXct("2023-05-23 19:39:10") & gps_data$datetime <= as.POSIXct("2023-05-23 19:42:10") |
gps_data$datetime >= as.POSIXct("2023-05-24 09:12:05") & gps_data$datetime <= as.POSIXct("2023-05-24 09:17:20") |
gps_data$datetime >= as.POSIXct("2023-05-24 14:50:15") & gps_data$datetime <= as.POSIXct("2023-05-24 14:52:20") |
gps_data$datetime >= as.POSIXct("2023-05-24 15:42:05") & gps_data$datetime <= as.POSIXct("2023-05-24 16:38:35") |
gps_data$datetime >= as.POSIXct("2023-05-25 14:35:55") & gps_data$datetime <= as.POSIXct("2023-05-25 14:40:25") |
gps_data$datetime >= as.POSIXct("2023-05-25 14:55:35") & gps_data$datetime <= as.POSIXct("2023-05-25 14:57:05") |
gps_data$datetime >= as.POSIXct("2023-05-25 15:14:50") & gps_data$datetime <= as.POSIXct("2023-05-25 15:19:15") |
gps_data$datetime >= as.POSIXct("2023-05-25 15:29:00") & gps_data$datetime <= as.POSIXct("2023-05-25 15:35:35") |
gps_data$datetime >= as.POSIXct("2023-05-25 20:06:20") & gps_data$datetime <= as.POSIXct("2023-05-25 20:52:00") |
gps_data$datetime >= as.POSIXct("2023-05-26 07:51:15") & gps_data$datetime <= as.POSIXct("2023-05-26 07:54:15") |
gps_data$datetime >= as.POSIXct("2023-05-26 08:47:15") & gps_data$datetime <= as.POSIXct("2023-05-26 12:17:30") |
gps_data$datetime >= as.POSIXct("2023-05-26 13:31:40") & gps_data$datetime <= as.POSIXct("2023-05-26 13:49:00") |
gps_data$datetime >= as.POSIXct("2023-05-26 14:27:10") & gps_data$datetime <= as.POSIXct("2023-05-26 17:20:10") |
gps_data$datetime >= as.POSIXct("2023-05-28 15:39:00") & gps_data$datetime <= as.POSIXct("2023-05-28 20:19:00") |
gps_data$datetime >= as.POSIXct("2023-05-28 20:57:05") & gps_data$datetime <= as.POSIXct("2023-05-28 21:00:00") |
gps_data$datetime >= as.POSIXct("2023-05-29 17:05:05") & gps_data$datetime <= as.POSIXct("2023-05-29 17:40:00") |
gps_data$datetime >= as.POSIXct("2023-05-29 18:13:30") & gps_data$datetime <= as.POSIXct("2023-05-29 18:18:45") |
gps_data$datetime >= as.POSIXct("2023-05-30 11:33:25") & gps_data$datetime <= as.POSIXct("2023-05-30 11:37:15") |
gps_data$datetime >= as.POSIXct("2023-05-31 17:44:25") & gps_data$datetime <= as.POSIXct("2023-05-31 17:48:00") |
gps_data$datetime >= as.POSIXct("2023-06-02 13:23:45") & gps_data$datetime <= as.POSIXct("2023-06-02 13:28:00") |
gps_data$datetime >= as.POSIXct("2023-06-02 13:55:35") & gps_data$datetime <= as.POSIXct("2023-06-02 14:19:00") |
gps_data$datetime >= as.POSIXct("2023-06-02 14:39:30") & gps_data$datetime <= as.POSIXct("2023-06-02 14:47:05") |
gps_data$datetime >= as.POSIXct("2023-06-02 15:16:45") & gps_data$datetime <= as.POSIXct("2023-06-02 15:20:25") |
gps_data$datetime >= as.POSIXct("2023-06-04 17:00:50") & gps_data$datetime <= as.POSIXct("2023-06-04 17:06:30") |
gps_data$datetime >= as.POSIXct("2023-06-04 17:17:20") & gps_data$datetime <= as.POSIXct("2023-06-04 18:17:00") |
gps_data$datetime >= as.POSIXct("2023-06-04 19:04:45") & gps_data$datetime <= as.POSIXct("2023-06-04 19:08:00") |
gps_data$datetime >= as.POSIXct("2023-06-05 12:51:35") & gps_data$datetime <= as.POSIXct("2023-06-05 12:56:40") |
gps_data$datetime >= as.POSIXct("2023-06-05 14:49:00") & gps_data$datetime <= as.POSIXct("2023-06-05 14:55:25") |
gps_data$datetime >= as.POSIXct("2023-06-05 15:28:00") & gps_data$datetime <= as.POSIXct("2023-06-05 15:36:00") |
gps_data$datetime >= as.POSIXct("2023-06-05 15:42:40") & gps_data$datetime <= as.POSIXct("2023-06-05 15:46:05") |
gps_data$datetime >= as.POSIXct("2023-06-06 20:00:05") & gps_data$datetime <= as.POSIXct("2023-06-06 20:34:25") |
gps_data$datetime >= as.POSIXct("2023-06-08 20:55:10") & gps_data$datetime <= as.POSIXct("2023-06-08 21:22:10") |
gps_data$datetime >= as.POSIXct("2023-06-09 13:54:10") & gps_data$datetime <= as.POSIXct("2023-06-09 17:14:20") |
gps_data$datetime >= as.POSIXct("2023-06-10 18:50:20") & gps_data$datetime <= as.POSIXct("2023-06-10 20:45:00") |
gps_data$datetime >= as.POSIXct("2023-06-10 21:03:15") & gps_data$datetime <= as.POSIXct("2023-06-10 21:05:40") |
gps_data$datetime >= as.POSIXct("2023-06-12 21:29:35") & gps_data$datetime <= as.POSIXct("2023-06-12 22:11:45") |
gps_data$datetime >= as.POSIXct("2023-06-15 21:43:25") & gps_data$datetime <= as.POSIXct("2023-06-15 21:45:00") |
gps_data$datetime >= as.POSIXct("2023-06-15 21:52:10") & gps_data$datetime <= as.POSIXct("2023-06-15 21:55:05") |
gps_data$datetime >= as.POSIXct("2023-06-15 21:58:05") & gps_data$datetime <= as.POSIXct("2023-06-15 21:59:55") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:02:00") & gps_data$datetime <= as.POSIXct("2023-06-15 22:07:15") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:14:45") & gps_data$datetime <= as.POSIXct("2023-06-15 22:19:00") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:25:25") & gps_data$datetime <= as.POSIXct("2023-06-15 22:28:20") |
gps_data$datetime >= as.POSIXct("2023-06-16 10:17:45") & gps_data$datetime <= as.POSIXct("2023-06-16 10:21:25")
condition_boat <-
gps_data$datetime >= as.POSIXct("2023-06-02 14:20:00") & gps_data$datetime <= as.POSIXct("2023-06-02 14:37:20")
condition_run <-
gps_data$datetime >= as.POSIXct("2023-05-21 20:24:10") & gps_data$datetime <= as.POSIXct("2023-05-21 20:24:55") |
gps_data$datetime >= as.POSIXct("2023-06-08 20:59:00") & gps_data$datetime <= as.POSIXct("2023-06-08 21:17:55") |
gps_data$datetime >= as.POSIXct("2023-06-15 21:45:10") & gps_data$datetime <= as.POSIXct("2023-06-15 21:52:00") |
gps_data$datetime >= as.POSIXct("2023-06-15 21:55:10") & gps_data$datetime <= as.POSIXct("2023-06-15 21:57:25") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:00:00") & gps_data$datetime <= as.POSIXct("2023-06-15 22:01:55") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:07:40") & gps_data$datetime <= as.POSIXct("2023-06-15 22:14:40") |
gps_data$datetime >= as.POSIXct("2023-06-15 22:19:45") & gps_data$datetime <= as.POSIXct("2023-06-15 22:20:55")
# Labelizing data
gps_data <- gps_data %>%
mutate(tmode_manual = case_when(condition_walk ~ 'walk',
condition_tram ~ 'tram',
condition_bus ~ 'bus',
condition_boat ~ 'boat',
condition_run ~ 'run',
is.na(gps_data$tmode_manual) == TRUE ~ 'unclassified'
))#here merge with gps tracker
mvmt_data <- rbind(posmo, gps_data)
unique(mvmt_data$tmode_manual)## [1] "unclassified" "bus" "train" "tram" "car"
## [6] "walk" "cable_car" "ski_lift" "ski" "t_bar"
## [11] "bike" "kick-scooter" "boat" "plane" "run"col_order <- c("source", "datetime", "tmode_manual" ,"E", "N", "H", "geometry")
mvmt_data <- mvmt_data[, col_order]
mvmt_data## Simple feature collection with 74871 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2508048 ymin: 1106915 xmax: 2696117 ymax: 1266310
## Projected CRS: CH1903+ / LV95
## # A tibble: 74,871 × 7
## source datetime tmode_manual E N H
## <chr> <dttm> <chr> <dbl> <dbl> <dbl>
## 1 posmo 2023-03-30 17:40:56 unclassified 2556120. 1202625. 490.
## 2 posmo 2023-03-30 17:41:05 unclassified 2555910. 1202492. 493.
## 3 posmo 2023-03-30 17:41:15 unclassified 2555629. 1202324. 495.
## 4 posmo 2023-03-30 17:41:32 unclassified 2555166. 1202092. 489.
## 5 posmo 2023-03-30 17:41:41 unclassified 2554897. 1201957. 493.
## 6 posmo 2023-03-30 17:45:33 unclassified 2550265. 1194674. 437.
## 7 posmo 2023-03-30 17:45:42 unclassified 2549951. 1194430. 449.
## 8 posmo 2023-03-30 17:45:42 unclassified 2549891. 1194429. 452.
## 9 posmo 2023-03-30 17:45:42 unclassified 2549951. 1194430. 449.
## 10 posmo 2023-03-30 17:45:51 unclassified 2549592. 1194179. 449.
## # ℹ 74,861 more rows
## # ℹ 1 more variable: geometry <POINT [m]>Map general overview - two users:
#map overview
mvmt_data_wgs <- mvmt_data %>% st_transform(crs = 4326)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addCircleMarkers(data = mvmt_data_wgs,
#opacity = 0.3,
radius = 0.2,
popup = mvmt_data_wgs$datetime,
color = 'blue') %>%
addLegend(position = 'topright',
colors = c('blue'),
labels = c('points'),
title = 'All points from GPS tracker and posmo')
# Plot the leaflet object m
mMap general overview - labelized segments:
#map overview
mvmt_data_wgs <- mvmt_data %>% st_transform(crs = 4326) %>% filter(tmode_manual != "classified")
# check overlapping days of labelized data
# mvmt_data_wgs %>%
# mutate(
# day = format(as.POSIXct(datetime), format = "%Y-%m-%d")) %>%
# group_by(day, source) %>%
# summarize(n()) %>% View()
#Palette
factpal <- colorFactor(topo.colors(2), domain =mvmt_data_wgs$source)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addCircleMarkers(data = mvmt_data_wgs,
#opacity = 0.3,
radius = 0.2,
popup = mvmt_data_wgs$datetime,
color = ~factpal(source)) %>%
#color = 'blue') %>%
addLegend(position = 'topright',
#colors = c('blue'),
#labels = c('points'),
pal = factpal,
values = mvmt_data_wgs$source,
title = 'Labelized points from GPS tracker and posmo')
# Plot the leaflet object m
mMap general overview - posmo user:
…
Map general overview - GPS tracker user:
…
Map general overview - per region (?):
…
Here we will compute all necessary values to determine mean of transport:
#compute trajectory derivatives
mvmt_data <-
mvmt_data %>%
group_by(source) %>%
mutate(
timelag = as.numeric(difftime(lead(datetime), datetime)),
steplength_m = sqrt((E-lead(E))^2 + (N-lead(N))^2), # Horizontal steplength
steplength_h = H-lead(H), # Add vertical steplength
speed_ms = abs(steplength_m)/timelag,
speed_v = abs(steplength_h)/timelag,
slope = (lead(H) - H)/steplength_m * 100
)
#remove timelag 0 -> recording errors
mvmt_data <- mvmt_data %>% filter(timelag != 0)# add regions
#Zurich
zh_ll <- c(2674900, 1244000)
zh_ur <- c(2692100, 1260300)
#Waadt
wt_ll <- c(2480000, 1127600)
wt_ur <- c(2575200, 1195100)
#wallis
ws_ll <- c(2538000, 1076875)
ws_ur <- c(2661500, 1153125)
mvmt_data$region <- NA
mvmt_data <- mvmt_data |>
mutate(
region = case_when(
E > zh_ll[1] & E < zh_ur[1] & N > zh_ll[2] & N < zh_ur[2] ~ 'Zurich',
E > wt_ll[1] & E < wt_ur[1] & N > wt_ll[2] & N < wt_ur[2] ~ 'Waadt',
E > ws_ll[1] & E < ws_ur[1] & N > ws_ll[2] & N < ws_ur[2] ~ 'Wallis'
)
)posmo sampling rate: 10 sec GPS sampling rate: 5 sec
#try on a sampled data
mvmt_data2 <- mvmt_data #|> filter(as.Date(datetime) == "2023-04-12")
#filter(as.Date(datetime) == "2023-05-04")
#filter(tmode_manual == "train")
mvmt_data2## Simple feature collection with 73025 features and 13 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2508048 ymin: 1106915 xmax: 2696117 ymax: 1256849
## Projected CRS: CH1903+ / LV95
## # A tibble: 73,025 × 14
## # Groups: source [2]
## source datetime tmode_manual E N H
## * <chr> <dttm> <chr> <dbl> <dbl> <dbl>
## 1 posmo 2023-03-30 17:40:56 unclassified 2556120. 1202625. 490.
## 2 posmo 2023-03-30 17:41:05 unclassified 2555910. 1202492. 493.
## 3 posmo 2023-03-30 17:41:15 unclassified 2555629. 1202324. 495.
## 4 posmo 2023-03-30 17:41:32 unclassified 2555166. 1202092. 489.
## 5 posmo 2023-03-30 17:41:41 unclassified 2554897. 1201957. 493.
## 6 posmo 2023-03-30 17:45:33 unclassified 2550265. 1194674. 437.
## 7 posmo 2023-03-30 17:45:42 unclassified 2549951. 1194430. 449.
## 8 posmo 2023-03-30 17:45:51 unclassified 2549592. 1194179. 449.
## 9 posmo 2023-03-30 17:47:15 unclassified 2547601. 1191098. 480.
## 10 posmo 2023-03-30 17:54:38 unclassified 2539084. 1181463. 434.
## # ℹ 73,015 more rows
## # ℹ 8 more variables: geometry <POINT [m]>, timelag <dbl>, steplength_m <dbl>,
## # steplength_h <dbl>, speed_ms <dbl>, speed_v <dbl>, slope <dbl>,
## # region <chr>#determine static points: choose an average distance to use as the threshold
mvmt_data_gap <- mvmt_data2 |>
ungroup() |>
mutate(gap = timelag > 30)
#Visualisation of gap / no gap
visu_gap_wgs <- mvmt_data_gap |> filter(gap) %>% st_transform(crs = 4326)
visu_no_gap_wgs <- mvmt_data_gap |> filter(!gap) %>% st_transform(crs = 4326)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addCircleMarkers(data = visu_no_gap_wgs,
radius = 0.2,
popup = paste(
" steplength: ", round(visu_no_gap_wgs$steplength_m,2),
" timelag: ", visu_no_gap_wgs$timelag,
" speed_ms: ", round(visu_no_gap_wgs$speed_ms,2),
" source: ", visu_no_gap_wgs$source
),
color = 'blue') %>%
addCircleMarkers(data = visu_gap_wgs,
radius = 0.2,
popup = paste(
" steplength: ", round(visu_gap_wgs$steplength_m,2),
" timelag: ", visu_gap_wgs$timelag,
" speed_ms: ", round(visu_gap_wgs$speed_ms,2),
" source: ", visu_gap_wgs$source
),
color = 'red') %>%
addLegend(position = 'topright',
colors = c('blue', 'red'),
labels = c('no gap', 'gap'),
title = 'Gap in the data')
# Plot the leaflet object m
m#function source: Exercice3
rle_id <- function(vec) {
x <- rle(vec)$lengths
as.factor(rep(seq_along(x), times = x))
}
mvmt_data_segmentized <- mvmt_data_gap %>%
mutate(
segment_ID_user = rle_id(source),
segment_ID_gap = 1, #to split segments when recording gap in it
segment_ID_tmode = rle_id(tmode_manual)
)
#increment segment_ID_gap for each TRUE
for (i in 2:nrow(mvmt_data_segmentized)) {
if (mvmt_data_segmentized$gap[i-1] == TRUE) {
mvmt_data_segmentized$segment_ID_gap[i] <- mvmt_data_segmentized$segment_ID_gap[i-1] + 1
} else {
mvmt_data_segmentized$segment_ID_gap[i] <- mvmt_data_segmentized$segment_ID_gap[i-1]
}
}
# ADD PRE-FINAL ID for segmentation
mvmt_data_segmentized <- mvmt_data_segmentized %>%
mutate(
segment_ID_both = paste(segment_ID_user, segment_ID_gap, segment_ID_tmode, sep = "-")
)
# mvmt_data_segmentized <- mvmt_data_gap %>%
# mutate(
# segment_ID_user = rle_id(source),
# segment_ID_gap = rle_id(gap), #to split segments when recording gap in it
# segment_ID_tmode = rle_id(tmode_manual),
# segment_ID_both = paste(segment_ID_user, segment_ID_gap, segment_ID_tmode, sep = "-")
# #segment_ID_both = as.numeric(segment_ID_user) * as.numeric(segment_ID_gap) * as.numeric(segment_ID_tmode)
# )
# Add duration per segment
# change the unit from mins to secs
mvmt_data_segmentized <- mvmt_data_segmentized %>%
group_by(segment_ID_both) %>%
mutate(duration_secs = as.integer(difftime(max(datetime), min(datetime), units = "secs")))
#specify temporal window
#60 seconds ?
#/!\ ordered and by user
#mvmt_data <- mvmt_data %>% arrange(desc(datetime))
#meanstep
mvmt_data_segmentized <- mvmt_data_segmentized |>
group_by(segment_ID_both) %>%
mutate(
nMinus5 = sqrt((lag(E, 5) - E)^2 + (lag(N, 5) - N)^2),
nMinus4 = sqrt((lag(E, 4) - E)^2 + (lag(N, 4) - N)^2),
nMinus3 = sqrt((lag(E, 3) - E)^2 + (lag(N, 3) - N)^2), #dist to pos -30s
nMinus2 = sqrt((lag(E, 2) - E)^2 + (lag(N, 2) - N)^2), #dist to pos -20s
nMinus1 = sqrt((lag(E, 1) - E)^2 + (lag(N, 1) - N)^2), #dist to pos -10 sec
nPlus1 = sqrt((E - lead(E, 1))^2 + (N - lead(N, 1))^2), #dist to pos +10 sec
nPlus2 = sqrt((E - lead(E, 2))^2 + (N - lead(N, 2))^2), #dist to pos +20 sec
nPlus3 = sqrt((E - lead(E, 3))^2 + (N - lead(N, 3))^2), #dist to pos +30 sec
nPlus4 = sqrt((E - lead(E, 4))^2 + (N - lead(N, 4))^2), #dist to pos +30 sec
nPlus5 = sqrt((E - lead(E, 5))^2 + (N - lead(N, 5))^2), #dist to pos +30 sec
)
mvmt_data_segmentized <- mvmt_data_segmentized |>
rowwise() |>
mutate(
stepMean_smallw = round(mean(c(nMinus1, nPlus1)),2),
stepMean_midw = round(mean(c(nMinus3, nMinus2, nMinus1, nPlus1, nPlus2, nPlus3)),2),
stepMean_bigw = round(mean(c(nMinus5, nMinus4, nMinus3, nMinus2, nMinus1, nPlus1, nPlus2, nPlus3, nPlus4, nPlus5)),2)
) |>
ungroup()
# # MAP VISUALISATION
# n_segs <- mvmt_data_segmentized$segment_ID_both %>% unique() %>% length()
# mvmt_data_wgs <- mvmt_data_segmentized %>% st_transform(crs = 4326)
#
# set.seed(2)
# factpal <- colorFactor(sample(rainbow(n_segs)), domain =mvmt_data_wgs$segment_ID_both)
#
# m <- leaflet() %>%
# #addTiles() %>% # Add default OpenStreetMap map tiles
# addProviderTiles("Esri.WorldGrayCanvas") %>%
# addCircleMarkers(data = mvmt_data_wgs,
# #opacity = 0.3,
# radius = 0.2,
# popup = paste("seg_ID: ", mvmt_data_wgs$segment_ID_both),
# color = ~factpal(segment_ID_both)) %>%
# addLegend(position = 'topright',
# #colors = c('red'),
# #labels = c('static'),
# pal = factpal,
# values = mvmt_data_wgs$segment_ID_both,
# title = 'Segments')
#
# # Plot the leaflet object m
# m#determine static points: choose an average distance to use as the threshold
mvmt_data_add_static <- mvmt_data_segmentized |>
group_by(segment_ID_both) %>%
#ungroup() |>
mutate(
#stepMean = ifelse(is.na(stepMean), 500, stepMean), # first and last points of window
static =
#timelag < 5.0
#(timelag_mean > 1.0 | timelag_mean < -1.0)
#timelag_mean != 0.0 |
#is.na(timelag_mean)
#speed_ms_mean < 0.02 & speed_ms_mean > -0.02
#stepMean_bigw < mean(stepMean_smallw, na.rm=TRUE),
# Optimize by adding 'or' conditions
stepMean_bigw < mean(stepMean_smallw, na.rm=TRUE) |
steplength_m < 0.5,
#stepMean < mean(steplength_m, na.rm=TRUE),
#mean(steplength_m, na.rm = TRUE)
static = ifelse(is.na(static), FALSE, static)
)
# Add percentage of static in each segment
mvmt_data_add_static <- mvmt_data_add_static |>
group_by(segment_ID_both) %>%
mutate(
static_percent = (sum(static))/(length(static))
# Optimize by only considering continuous static points
)
#mvmt_data %>% group_by(source) %>% summarize(mean = mean(stepMean)) %>% View()
# #Visualisation of static / non static:
# visu_static_wgs <- mvmt_data_add_static |> filter(static) %>% st_transform(crs = 4326)
#
# visu_no_static_wgs <- mvmt_data_add_static |> filter(!static) %>% st_transform(crs = 4326)
#
# m <- leaflet() %>%
# addTiles() %>% # Add default OpenStreetMap map tiles
# addCircleMarkers(data = visu_no_static_wgs,
# #opacity = 0.3,
# radius = 0.2,
# popup = paste(
# "step_mean: ", visu_no_static_wgs$stepMean_midw,
# " steplength: ", round(visu_no_static_wgs$steplength_m,2),
# " timelag: ", visu_no_static_wgs$timelag,
# " speed_ms: ", round(visu_no_static_wgs$speed_ms,2),
# " source: ", visu_no_static_wgs$source
# ),
# color = 'blue') %>%
# addCircleMarkers(data = visu_static_wgs,
# #opacity = 0.3,
# radius = 0.2,
# popup = paste(
# "step_mean: ", visu_static_wgs$stepMean_midw,
# " steplength: ", round(visu_static_wgs$steplength_m,2),
# " timelag: ", visu_static_wgs$timelag,
# " speed_ms: ", round(visu_static_wgs$speed_ms,2),
# " source: ", visu_static_wgs$source
# ),
# color = 'red') %>%
# addLegend(position = 'topright',
# colors = c('blue', 'red'),
# labels = c('non static', 'static'),
# title = 'static vs non static')
#
# # Plot the leaflet object m
# m
# visu_no_static |>
# ggplot(aes(E, N)) +
# geom_path() +
# geom_point() +
# coord_fixed() +
# theme(legend.position = "bottom")#mvmt_data_add_static %>% dplyr::select(c("segment_ID_static", "segment_ID_tmode", "segment_ID_user", "source", "segment_ID_gap", "segment_ID_both")) %>% st_drop_geometry() %>% unique() %>% View()
#mvmt_data %>% dplyr::select(c("segment_ID_static", "segment_ID_tmode")) %>% st_drop_geometry() %>% unique() %>% View()
# filter static and small segments
mvmt_data_filtered <- mvmt_data_add_static %>%
group_by(segment_ID_both, source, tmode_manual, duration_secs, static, static_percent) %>%
# remove static segments
filter(static == FALSE) %>%
#remove the too small segments (duration < 30 s)
filter(duration_secs > 30)
# we should also filter segments with less than ~5 points (?)
# MAP VISUALISATION
# n_segs <- mvmt_data_filtered$segment_ID_both %>% unique() %>% length()
# mvmt_data_wgs <- mvmt_data_filtered %>% st_transform(crs = 4326)
#
# set.seed(2)
# factpal <- colorFactor(sample(rainbow(n_segs)), domain =mvmt_data_wgs$segment_ID_both)
#
# m <- leaflet() %>%
# #addTiles() %>% # Add default OpenStreetMap map tiles
# addProviderTiles("Esri.WorldGrayCanvas") %>%
# addCircleMarkers(data = mvmt_data_wgs,
# #opacity = 0.3,
# radius = 0.2,
# popup = paste("seg_ID: ", mvmt_data_wgs$segment_ID_both),
# color = ~factpal(segment_ID_both)) %>%
# addLegend(position = 'topright',
# #colors = c('red'),
# #labels = c('static'),
# pal = factpal,
# values = mvmt_data_wgs$segment_ID_both,
# title = 'Segments - filtered statics and small')
#
# # Plot the leaflet object m
# mmvmt_data_split <- mvmt_data_filtered %>%
group_by(segment_ID_both) %>%
mutate(
#remove timelag to next segment
timelag = case_when(timelag > 30 ~ 0,
timelag <= -20 ~ 0,
.default = timelag),
duration_index = case_when(
(duration_secs > 100) & (duration_secs %% 100) >= 50 ~ ceiling(cumsum(timelag)/100),
(duration_secs > 100) & (duration_secs %% 100) < 50 ~ ceiling((cumsum(timelag)/100)-1),
.default = 0
),
segment_ID_final = paste(segment_ID_both, duration_index, sep = "-")
)
mvmt_data_split %>% View()mvmt_seg_final <- mvmt_data_split %>%
dplyr::select(-c("segment_ID_user", "segment_ID_both", "segment_ID_gap", "duration_index", "segment_ID_tmode", "nMinus5", "nMinus4", "nMinus3", "nMinus2", "nMinus1", "nPlus1", "nPlus2", "nPlus3", "nPlus4", "nPlus5"
))Compute trajectories derivatives for each segment –> to do after annotation.
# SUMMARIZE
mvmt_seg_summary <- mvmt_seg_final %>%
group_by(segment_ID_final, static_percent, duration_secs, tmode_manual) %>%
summarize(
# Total segments
#time_duration = duration_secs,
space_duration = sqrt((sum(abs(steplength_m))^2 + (sum(abs(steplength_h))^2))),
stepMean_midw_mean = mean(stepMean_midw, na.rm=TRUE),
# Step length
steplength_mean = mean(steplength_m),
steplength_sd = sd(steplength_m),
# Horizontal speed
speed_h_mean = mean(speed_ms),
speed_h_sd = sd(speed_ms),
speed_h_range = max(speed_ms) - min(speed_ms),
# Vertical speed
speed_v_mean = mean(speed_v),
speed_v_sd = sd(speed_v),
speed_v_range = max(speed_v) - min(speed_v),
# Sinuosity
# Acceleration
# Geometry
lst_geometry = list(geometry))## `summarise()` has grouped output by 'segment_ID_final', 'static_percent',
## 'duration_secs'. You can override using the `.groups` argument.mvmt_seg_summary %>% View()/! dissolve the shapefiles first makes the calculation quickest.
done: - check buffer distance with other examples - calculate % per segment to attributes FALSE/TRUE to the segment -> add it in mvmt_data_summarized
Q: -> faire convex-hull autour des points pour couper les datasets et limiter le tps de calcul? (sur R)
#ALL
# 1) RAILS
rails_shp <- read_sf(here(data_folder, "railways", "rails_train_dissolved.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(rails_shp, join = st_is_within_distance, 30)
mvmt_seg_final$isClosetoRails <- !is.na(joined_data$xtf_id)
# 2) TRAMS
trams_shp <- read_sf(here(data_folder, "railways", "trams_dissolved.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(trams_shp, join = st_is_within_distance, 20)
mvmt_seg_final$isClosetoTrams <- !is.na(joined_data$xtf_id)
# 3) BUS STOPS
bus_shp <- read_sf(here(data_folder, "stops", "bus_stops_dissolved.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(bus_shp, join = st_is_within_distance, 50)
mvmt_seg_final$isClosetoBus <- !is.na(joined_data$fid)
# 4) HIGHWAYS
roads_shp <- read_sf(here(data_folder, "roads", "roads_dissolved.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(roads_shp, join = st_is_within_distance, 20)
mvmt_seg_final$isClosetoHighways <- !is.na(joined_data$UUID)
# 5) LAKES
lakes_shp <- read_sf(here(data_folder, "lakes", "large_lakes_dissolved.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(lakes_shp, join = st_is_within_distance, 1)
mvmt_seg_final$isClosetoLakes <- !is.na(joined_data$UUID)
# 6) SKI LIFTS AND CABLE CARS
cables_shp <- read_sf(here(data_folder, "ski_lift_cable_car", "skilift_cablecar.shp")) %>% st_zm()
joined_data <- mvmt_seg_final %>%
st_join(cables_shp, join = st_is_within_distance, 10)
mvmt_seg_final$isClosetoCables <- !is.na(joined_data$UUID)
# 7) HIGH ALTITUDES
mvmt_seg_final <- mvmt_seg_final %>% mutate(
isHighAltitude = case_when(H >= 1200.0 ~ TRUE,
.default = FALSE)
)# SUMMARIZE ANNOTATIONS
mvmt_seg_annotations_summarized <- mvmt_seg_final %>%
group_by(segment_ID_final, static_percent, duration_secs, tmode_manual) %>%
summarize(
isClosetoLakes = (sum(isClosetoLakes) / n() * 100 ) > 0,
isClosetoRails = (sum(isClosetoRails) / n() * 100 ) > 70.0,
isClosetoBus = (sum(isClosetoBus) / n() * 100 ) > 30.0,
isClosetoTrams = (sum(isClosetoTrams) / n() * 100 ) > 70.0,
isClosetoHighways = (sum(isClosetoHighways) / n() * 100 ) > 80.0,
isClosetoCables = (sum(isClosetoCables) / n() * 100 ) > 80.0,
isHighAltitude = (sum(isHighAltitude) / n() * 100 ) > 80.0
)## `summarise()` has grouped output by 'segment_ID_final', 'static_percent',
## 'duration_secs'. You can override using the `.groups` argument.mvmt_seg_annotations_summarized %>% View()#VISUALISATION
#Visualisation of proximity for rails:
# isClosetoRails
# isClosetoTrams
# isClosetoLakes
# isClosetoHighways
# isClosetoCables
# isHighAltitude
visu_true <- mvmt_seg_final |> filter(isClosetoRails) %>% st_transform(crs = 4326)
visu_false <- mvmt_seg_final |> filter(!isClosetoRails) %>% st_transform(crs = 4326)
wgs <- rails_shp %>% st_transform(crs = 4326)
#wgs <- trams_shp %>% st_transform(crs = 4326)
#wgs <- lakes_shp %>% st_transform(crs = 4326)
#wgs <- roads_shp %>% st_transform(crs = 4326)
#wgs <- cables_shp %>% st_transform(crs = 4326)
# ALTITUDE = NO WGS
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addPolylines(
data = wgs,
color = 'green'
) %>%
addCircleMarkers(data = visu_false,
radius = 0.2,
popup = paste(
"tmode: ", visu_false$tmode_manual, ". ID: ", visu_false$segment_ID_final
),
color = 'red') %>%
addCircleMarkers(data = visu_true,
radius = 0.2,
popup = paste(
"tmode: ", visu_true$tmode_manual, ". ID: ", visu_false$segment_ID_final
),
color = 'blue') %>%
addLegend(position = 'topright',
colors = c('blue', 'red'),
labels = c('close', 'not close'),
title = 'Proximity to environment')
# Plot the leaflet object m
m